Is there a way to use Accenture DevOps Platform ADOP in the local machine without using any server

Question

I just want to know if there's any way to deploy ADOP on the local machine instead of a server. I was able to deploy it using aws.

Answer 1

It is definitely possible to deploy ADOP on a local machine, but it is a tad bit harder than doing it on a server. Here's a quickstart guide you can use to get started.

 https://accenture.github.io/adop-docker-compose/docs/quickstart/ 

Do check the prerequisites before getting started. From ADOP documentation:

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NB. the instructions will also work in anywhere supported by Docker Machine, just follow the relevant Docker Machine instructions for your target platform and then start at step 3 below and (you can set the VPC_ID to NA).

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Answer 2

Follow these steps:

• Create a VPC using the VPC wizard in the AWS console by selecting the first option with 1 public subnet.
• VPC with a Single Public Subnet” page give your VPC a meaningful name and specify the availability zone as ‘a’, e.g. select eu-west-1a from the pulldown.
• Once the VPC is created note the VPC ID (e.g. vpc-1ed3sfgw)
• Clone this repository adop-docker-compose and then in a terminal window ans run
•            $ ./quickstart.sh

•            Usage: ./quickstart.sh -t aws
                           -m <MACHINE_NAME> 
                           -c <VPC_ID>
                           -r <REGION>(optional)
                           -z <VPC_AVAIL_ZONE>(optional)
                           -a <AWS_ACCESS_KEY>(optional)
                           -s <AWS_SECRET_ACCESS_EY>(optional)
                           -u <INITIAL_ADMIN_USER>
                           -p <INITIAL_ADMIN_PASSWORD>(optional) ...

• You will need to supply the type of machine to create, a machine name, the target VPC, a username and password
• you should get the following output  - SUCCESS, your new ADOP instance is ready!
• Log in using the username and password you specified in the quickstart script.
• Update the docker-machine security group in the AWS console

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